
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


snake_case__					=				logging.getLogger(__name__)







@dataclass(frozen=_lowerCAmelCase       )
class 				UpperCAmelCase						:
					a__:			Dict      	=					42
					a__:			int      	=					42
					a__:			List[Any]      	=					None
					a__:			Optional[int]      	=					None
					a__:			Tuple      	=					None







@dataclass(frozen=_lowerCAmelCase       )
class 				UpperCAmelCase						:
					a__:			Optional[Any]      	=					42
					a__:			List[Any]      	=					None
					a__:			int      	=					None
					a__:			Union[str, Any]      	=					None
					a__:			Optional[Any]      	=					None


if is_torch_available():
				import torch
				from torch.utils.data import Dataset







				class 				UpperCAmelCase						( _lowerCAmelCase       ):
									a__:			int      	=					42

									def __init__(      self   :       Tuple			,   lowerCAmelCase   :       Any			,   lowerCAmelCase   :       List[str]			,   lowerCAmelCase   :       List[str]			,   lowerCAmelCase   :       Optional[Any] = None			,   lowerCAmelCase   :       Dict=False			,   lowerCAmelCase   :       Optional[int] = False			,   ):
															lowercase							:     Optional[Any]									=	hans_processors[task]()

															lowercase							:     Dict									=	os.path.join(
															    SCREAMING_SNAKE_CASE_			,   '''cached_{}_{}_{}_{}'''.format(
															        '''dev''' if evaluate else '''train'''			,   tokenizer.__class__.__name__			,   str(SCREAMING_SNAKE_CASE_       )			,   SCREAMING_SNAKE_CASE_			,   )			,   )
															lowercase							:     Dict									=	processor.get_labels()
															if tokenizer.__class__ in (
															    RobertaTokenizer,
															    RobertaTokenizerFast,
															    XLMRobertaTokenizer,
															    BartTokenizer,
															    BartTokenizerFast,
															):
																					# HACK(label indices are swapped in RoBERTa pretrained model)
																					lowercase							:     Tuple									=	label_list[2], label_list[1]
															lowercase							:     str									=	label_list

															# Make sure only the first process in distributed training processes the dataset,
															# and the others will use the cache.
															lowercase							:     int									=	cached_features_file + """.lock"""
															with FileLock(SCREAMING_SNAKE_CASE_       ):
																					if os.path.exists(SCREAMING_SNAKE_CASE_       ) and not overwrite_cache:
																											logger.info(f'''Loading features from cached file {cached_features_file}'''       )
																											lowercase							:     Dict									=	torch.load(SCREAMING_SNAKE_CASE_       )
																					else:
																											logger.info(f'''Creating features from dataset file at {data_dir}'''       )

																											lowercase							:     Union[str, Any]									=	(
																											    processor.get_dev_examples(SCREAMING_SNAKE_CASE_       ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_       )
																											)

																											logger.info('''Training examples: %s'''			,   len(SCREAMING_SNAKE_CASE_       )       )
																											lowercase							:     List[Any]									=	hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_			,   SCREAMING_SNAKE_CASE_			,   SCREAMING_SNAKE_CASE_			,   SCREAMING_SNAKE_CASE_       )
																											logger.info('''Saving features into cached file %s'''			,   SCREAMING_SNAKE_CASE_       )
																											torch.save(self.features			,   SCREAMING_SNAKE_CASE_       )

									def __len__(      self   :       Tuple       ):
															return len(self.features       )

									def __getitem__(      self   :       List[Any]			,   lowerCAmelCase   :       Optional[int]       ):
															return self.features[i]




									def 							_lowerCAmelCase						(      self   :       Optional[int]       ):
															return self.label_list


if is_tf_available():
				import tensorflow as tf







				class 				UpperCAmelCase						:
									a__:			str      	=					42

									def __init__(      self   :       Optional[Any]			,   lowerCAmelCase   :       int			,   lowerCAmelCase   :       Optional[Any]			,   lowerCAmelCase   :       Tuple			,   lowerCAmelCase   :       Union[str, Any] = 128			,   lowerCAmelCase   :       Any=False			,   lowerCAmelCase   :       Optional[Any] = False			,   ):
															lowercase							:     Union[str, Any]									=	hans_processors[task]()
															lowercase							:     List[Any]									=	processor.get_labels()
															if tokenizer.__class__ in (
															    RobertaTokenizer,
															    RobertaTokenizerFast,
															    XLMRobertaTokenizer,
															    BartTokenizer,
															    BartTokenizerFast,
															):
																					# HACK(label indices are swapped in RoBERTa pretrained model)
																					lowercase							:     List[str]									=	label_list[2], label_list[1]
															lowercase							:     Dict									=	label_list

															lowercase							:     List[str]									=	processor.get_dev_examples(SCREAMING_SNAKE_CASE_       ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_       )
															lowercase							:     Any									=	hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_			,   SCREAMING_SNAKE_CASE_			,   SCREAMING_SNAKE_CASE_			,   SCREAMING_SNAKE_CASE_       )

															def gen():
																					for ex_index, ex in tqdm.tqdm(enumerate(self.features       )			,   desc='''convert examples to features'''       ):
																											if ex_index % 1_0000 == 0:
																																	logger.info('''Writing example %d of %d''' % (ex_index, len(SCREAMING_SNAKE_CASE_       ))       )

																											yield (
																											    {
																											        "example_id": 0,
																											        "input_ids": ex.input_ids,
																											        "attention_mask": ex.attention_mask,
																											        "token_type_ids": ex.token_type_ids,
																											    },
																											    ex.label,
																											)

															lowercase							:     List[str]									=	tf.data.Dataset.from_generator(
															    SCREAMING_SNAKE_CASE_			,   (
															        {
															            '''example_id''': tf.intaa,
															            '''input_ids''': tf.intaa,
															            '''attention_mask''': tf.intaa,
															            '''token_type_ids''': tf.intaa,
															        },
															        tf.intaa,
															    )			,   (
															        {
															            '''example_id''': tf.TensorShape([]       ),
															            '''input_ids''': tf.TensorShape([None, None]       ),
															            '''attention_mask''': tf.TensorShape([None, None]       ),
															            '''token_type_ids''': tf.TensorShape([None, None]       ),
															        },
															        tf.TensorShape([]       ),
															    )			,   )

									def 							_lowerCAmelCase						(      self   :       Dict       ):
															return self.dataset

									def __len__(      self   :       Optional[Any]       ):
															return len(self.features       )

									def __getitem__(      self   :       int			,   lowerCAmelCase   :       str       ):
															return self.features[i]




									def 							_lowerCAmelCase						(      self   :       str       ):
															return self.label_list







class 				UpperCAmelCase						( _lowerCAmelCase       ):

					def 							_lowerCAmelCase						(      self   :       int			,   lowerCAmelCase   :       str       ):
											return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_			,   '''heuristics_train_set.txt'''       )       )			,   '''train'''       )

					def 							_lowerCAmelCase						(      self   :       List[Any]			,   lowerCAmelCase   :       Optional[int]       ):
											return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_			,   '''heuristics_evaluation_set.txt'''       )       )			,   '''dev'''       )

					def 							_lowerCAmelCase						(      self   :       Tuple       ):
											return ["contradiction", "entailment", "neutral"]




					def 							_lowerCAmelCase						(      self   :       List[Any]			,   lowerCAmelCase   :       Dict			,   lowerCAmelCase   :       Optional[int]       ):
											lowercase							:     Optional[Any]									=	[]
											for i, line in enumerate(SCREAMING_SNAKE_CASE_       ):
																	if i == 0:
																							continue
																	lowercase							:     Tuple									=	"""%s-%s""" % (set_type, line[0])
																	lowercase							:     int									=	line[5]
																	lowercase							:     int									=	line[6]
																	lowercase							:     List[str]									=	line[7][2:] if line[7].startswith('''ex'''       ) else line[7]
																	lowercase							:     Any									=	line[0]
																	examples.append(InputExample(guid=SCREAMING_SNAKE_CASE_			,   text_a=SCREAMING_SNAKE_CASE_			,   text_b=SCREAMING_SNAKE_CASE_			,   label=SCREAMING_SNAKE_CASE_			,   pairID=SCREAMING_SNAKE_CASE_       )       )
											return examples





def 							lowerCamelCase_   (      UpperCAmelCase_				:					List[InputExample]				,      UpperCAmelCase_				:					List[str]				,      UpperCAmelCase_				:					int				,      UpperCAmelCase_				:					PreTrainedTokenizer				,      ):
						lowercase							:     List[str]									=	{label: i for i, label in enumerate(lowerCAmelCase__							)}

						lowercase							:     Optional[int]									=	[]
						for ex_index, example in tqdm.tqdm(enumerate(lowerCAmelCase__							)				,      desc='''convert examples to features'''							):
												if ex_index % 1_00_00 == 0:
																		logger.info('''Writing example %d''' % (ex_index)							)

												lowercase							:     Tuple									=	tokenizer(
												    example.text_a				,      example.text_b				,      add_special_tokens=lowerCAmelCase__				,      max_length=lowerCAmelCase__				,      padding='''max_length'''				,      truncation=lowerCAmelCase__				,      return_overflowing_tokens=lowerCAmelCase__				,      )

												lowercase							:     Optional[Any]									=	label_map[example.label] if example.label in label_map else 0

												lowercase							:     Tuple									=	int(example.pairID							)

												features.append(InputFeatures(**lowerCAmelCase__				,      label=lowerCAmelCase__				,      pairID=lowerCAmelCase__							)							)

						for i, example in enumerate(examples[:5]							):
												logger.info('''*** Example ***'''							)
												logger.info(f'''guid: {example}'''							)
												logger.info(f'''features: {features[i]}'''							)

						return features


snake_case__					=				{
    'hans': 3,
}




snake_case__					=				{
    'hans': HansProcessor,
}







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a      :  Optional[int]			     =			logging.getLogger(__name__)







@dataclass
class 				_a				(   _lowerCAmelCase					):
			A      		=	field(
			    default=0.0 ,		metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''whether to use adafactor'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default='''linear''' ,		metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}'''} ,		)






